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Chapter Three discusses violations of basic assumptions in the Classical Linear Regression Model (CLRM), focusing on heteroskedasticity and multicollinearity. Heteroskedasticity occurs when error term variances are not constant, leading to inefficient OLS estimators and invalid significance tests, while multicollinearity arises from high correlations among explanatory variables, complicating the isolation of their individual effects. The chapter also outlines methods for detecting these issues and suggests

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